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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Global Journal of System and Computer Engineering
S.Intam, Rezki Nurul Jariah
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Sistem Klasifikasi Jenis Sampah Berdasarkan Kombinasi Fitur Warnac Tekstur Menggunakan Artifical Neural Network Berbasis Pengolahan Citra Digital S.Intam, Rezki Nurul Jariah; Raihan, Ahmad; Alfajri, Muh; Kaswar , Andi Baso; Andayani, Dyah Darma; Asnidar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 2: April 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241128330

Abstract

Pengelolaan sampah merupakan isu multisektor yang memiliki dampak dalam berbagai aspek kehidupan manusia. Sampah sangat penting untuk dikelola dengan baik untuk meminimalisir dampak negated dan memaksimalkan dampak positifnya pada masyarakat. Dalam pengelolaan sampah, sampah dibagi ke dalam dua jenis yaitu sampah organik dan anorganik. Agar dapat dikelola dengan mudah dan efektif, sampah harus dikelompokkan berdasarkan jenisnya. Namun, diberbagai tempat pembuangan sampah, dua jenis sampah tersebut masih tercampur antara satu sama lain. Oleh karena itu, pada penelitian ini dilakukan implementasi teknologi pengolahan citra digital untuk pemilahan sampah menggunakan metode Artifical Neural Network. Adapun metode yang diusulkan terdiri atas enam tahap yaitu, tahap akuisisi citra, preprocessing, segmentasi, morfologi, ekstraksi fitur, dan klasifikasi berdasarkan model jaringan syaraf tiruan yang telah dilatih. Pada penelitian ini juga, dilakukan beberapa skenario pengujian untuk menentukan kombinasi fitur yang memiliki tingkat akurasi terbaik. Hasil pengujian menunjukkan 2 kombinasi fitur terbaik yaitu fitur warna HSV, LAB dan fitur tekstur. Berdasarkan hasil pengujian terhadap 210 citra uji, diperoleh rata-rata precision 84,11%, recall 84.16%, F1-Score 84,08% dan akurasi keseluruhan mencapai 84%.  Hasil tersebut menunjukkan bahwa pengelompokan jenis sampah telah dilakukan dengan cukup akurat.
Application of Advanced Encryption Standard (AES) Algorithm in E-Commerce Login System for User Data Security Ifani, Aulyah Zakilah; S.Intam, Rezki Nurul Jariah; Syair, Andi Irfandi; Husnawati, Husnawati
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1511

Abstract

E-commerce becomes an electronic media that uses a login system used by users. User data in the form of usernames and passwords is vulnerable to hacking. One technique to improve user security is the implementation of AES algorithms on login systems in E-Commerce applications. The purpose of this study is to apply the AES algorithm in the login system of e-commerce websites and analyze the improvement of information security for users after the implementation is carried out. The research method used is an experiment with the application of the use of the AES algorithm before and after. Therefore, the application of the AES algorithm on the login system of e-commerce websites can be used as a solution to improve user data security. Testing using Wireshark and Burpsuite tools. The results obtained are that AES successfully secures the username and password on the e-commerce login system .
Klasifikasi Mahasiswa Berprestasi Menggunakan Fuzzy C-Means Dan Naive Bayes S.Intam, Rezki Nurul Jariah; Wulandari; Risal, Andi Akram Nur; Surianto, Dewi Fatmarani
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3666

Abstract

Success in the world of education is often associated with successful academic achievements. Therefore, processing information is very important to determine the selection of students who excel. However, study programs and student services often face difficulties in recognizing students who have achievements. In this research, outstanding students from the Faculty of Engineering, Makassar State University were determined using the Naive Bayes classification method combined with the Fuzzy C-Means (FCM) method to identify data patterns before classification. The criteria measured are GPA, achievements achieved, organizations attended, and the number of Semester Credit Units (SKS) that have been programmed. By using the Confusion Matrix, the evaluation results show an accuracy level of 98%, recall of 97%, precision of 100%, and F1-Score of 99%.
Co-Authors Alfajri, Muh Andi Akram Nur Risal Asnidar Aulyah Zakilah Ifani Dewi Fatmarani Surianto Husnawati Husnawati Kaswar , Andi Baso Raihan, Ahmad Syair, Andi Irfandi WULANDARI